A number of MRI techniques have been developed to quantify myocardial motion, including myocardial tagging (see, e.g., Zerhouni, E. A., Parish, D. M., Rogers, W. J., Yang, A. Shapiro, E. P., “Human heart: tagging with MR imaging—a method for noninvasive assessment of myocardial motion,” Radiology 163(1), 59-63, 1998; Axel, L., Dougherty, L., “MR imaging of motion with spatial modulation of magnetization,” Radiology 171, 841-845, 1989), phase contrast (PC) velocity encoding (see, e.g., van Dijk, P., “Direct cardiac MNR imaging of heart wall and blood flow velocity,” Journal of computer assisted tomography 8(3), 429-436, 1984; Bryant, D., Payne, J., Firmin, D., Longmore, D., “Measurement of flow with NMR imaging using gradient pulse and phase difference technique,” Journal of computer assisted tomography 8(4), 588-593, 1984), and more recently, displacement encoding with stimulated echoes (DENSE) (Aletras, A., Ding, S., Balaban, R., Wen, H., “DENSE: displacement encoding with stimulated echoes in cardiac functional MRI,” Journal of Magnetic Resonance 137, 247-252, 1989).
These techniques are potentially useful for the diagnosis, prognosis and management of heart disease, but their clinical use is currently limited by lack of automation.
Defining epicardial and endocardial contours is an integral step in quantifying regional cardiac wall motion. For cine DENSE these are typically manually delineated for all cardiac phases, which is a laborious process and is currently the most time-consuming component of the cine-DENSE image analysis. Even for myocardial tagging, for which the processing is less-automated than cine DENSE, Montillo, A., Metaxas, D., Axel, L., “Automated segmentation of the left and right ventricles in 4-D cardiac SPAMM images,” International Society and Conference Series on Medical Image Computing and Computer-Assisted Intervention (MICCAI), LNCS 2488, 620-633, 2002, estimates that 80 percent of the time required to analyze tagged datasets involves outlining the contours of the ventricles. Automated myocardial contour detection techniques based on image intensity are ill-suited to cine DENSE because: 1. boundaries between the myocardium and adjacent tissue (e.g. the liver) are often indiscernible; 2. a T1-related decay in signal-to-noise ratio (SNR) with time is often present; and 3. high signal is present in the encoded blood of the first few frames, before it is washed out of the image plane.
A number of advances have been made towards automating image segmentation for myocardial tagging and velocity encoding, but no segmentation algorithm currently exists for cine DENSE. Some early attempts to automate contour detection for tagged images made use of manually-guided active geometry Kumar, S., Goldgof, D., Automatic tracking of SPAMM grid and the estimation of deformation parameters from cardiac MR images. IEEE Transactions on Medical Imaging 13 (1), 122-132, 1994.
Young, A. A., Kraitchman, D. L., Dougherty, L., Axel, L., “Tracking and finite element analysis of stripe deformation in magnetic resonance tagging,” IEEE Transactions on Medical Imaging 14(3), 413-421, 1995; Guttmann, M. A., Prince, J. L., McVeigh, E. R., “Tag and contour detection in tagged MR images of the left ventricle,” IEEE Transactions on Medical Imaging 13(1), 74-88, 1994), presented a completely automated approach based on a hierarchy of image processing steps, including morphological closing to eliminate tag lines, candidate point selection based on large image gradients, and a dynamic programming algorithm that minimizes several cost functions. Their method performed well for detection of the endocardial boundary in both short- and long-axis images, but limited manual correction was sometimes required for the epicardium. Montillo et al. (2002) developed a completely automated method that uses volumetric data of stacked short-axis tagged images. For endocardial segmentation they used 3D greyscale morphological opening to identify the blood pools, and then applied a binary 3D cylinder shaped structuring element. The epicardial contour was found by filling the tags using a linear structuring element, and expanding an active contour model from the endocardium. The average distance error of the segmented epicardial contour compared to manually-drawn contours was reported to be less than 1.2 pixels. Montillo, A., Metaxas, D., Axel, L., “Automated model-based segmentation of the left and right ventricles in tagged cardiac MRI,” International Society and Conference Series on Medical Image Computing and Computer-Assisted Intervention (MICCAI), LNCS Vol 2878, 507-515, 2003, furthered this work and developed an automated, deformable model-based method to segment the left and right ventricles in 4D tagged MR. They compared the results with 2D manually drawn contours and for the epicardium found that 50 percent of segmented points were within 2.1 pixels (a pixel is 1×1 mm2), and 90 percent were within 6.3 pixels of manually identified regions.
Although PC velocity encoded magnitude images are not interrupted by tag lines, they are “white blood” sequences and thus suffer from a lack of contrast between blood and myocardium. A useful feature in PC velocity encoding is that both magnitude and phase images can be used to assist the segmentation process, whereby edge and velocity information is obtained from the magnitude and phase images, respectively. It is worth remembering that magnitude and phase images are perfectly co-registered since they are directly derived from the complex MRI data. The use of velocity-constrained active contour models has been developed for computer vision (see, e.g., Peterfreund, N., “The velocity snake: deformable contour for tracking in spatio-velocity space, “Computer Vision and Image Understanding 73(3), 346-356, 1999) and medical image analysis (see, e.g., Li, M, Kambhamettu, C, “Motion-based post processing of deformable contours,” In: Proc. Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), 2002). A few more recent studies have used PC velocity information of the myocardium to improve the accuracy of sequential frame segmentation of the myocardial boundaries.
Wong, A. L. N., Liu, H., Shi, P., “Segmentation of the Myocardium Using Velocity Field Constrained Front Propagation,” Proceedings of the 6th IEEE Workshop on Applications of Computer Vision, p 84, 2002, presents a myocardial segmentation technique where velocity fields from PC velocity encoding are combined with a two-stage front propagation technique. The front propagation is done using fast marching followed by a level set method. Velocity information is then incorporated based on the front distribution at the preceding cardiac phase. In addition, they used a local measure of phase coherence to eliminate noisy velocity measurements. Although only initial qualitative results are presented, it is clear that the velocity constrained contours for both the epi- and endocardium are superior to those obtained using intensity information alone.
In a related study, Cho, J., Benkeser, P. J., “Cardiac segmentation by a velocity-aided active contour model,” Computerized Medical Imaging and Graphics 30, 31-41, 2006, proposed a velocity-aided cardiac segmentation method for the endocardium based on a modified active contour model. They introduced a new image force called the tensor-based orientation gradient force. Boundary matching descriptors were used to provide a quantitative measure of shape similarity between estimated and manually-drawn boundaries. A significant improvement was seen when the orientation gradient force was taken into account.
It would be advantageous to provide a segmentation method that uses tissue tracking based on the encoded motion to project a single manually-defined set of contours through time. Such a method may rely exclusively on the cine DENSE phase images to thereby benefit from a unique noise-immunity.